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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x)

One of the fundamental factors contributing to the spatiotemporal inaccuracy in climate modeling is the mapping of solution field data between different discretizations and numerical grids used in the coupled component models. The typical climate computational workflow involves evaluation and serialization of the remapping weights during the preprocessing step, which is then consumed by the coupled driver infrastructure during simulation to compute field projections. Tools like Earth System Modeling Framework (ESMF) and TempestRemap offer capability to generate conservative remapping weights, while the Model Coupling Toolkit (MCT) that is utilized in many production climate models exposes functionality to make use of the operators to solve the coupled problem. However, such multistep processes present several hurdles in terms of the scientific workflow and impede research productivity. In order to overcome these limitations, we present a fully integrated infrastructure based on the Mesh Oriented datABase (MOAB) library, which allows for a complete description of the numerical grids and solution data used in each submodel. Through a scalable advancing-front intersection algorithm, the supermesh of the source and target grids are computed, which is then used to assemble the high-order, conservative, and monotonicity-preserving remapping weights between discretization specifications. The Fortran-compatible interfaces in MOAB are utilized to directly link the submodels in the Energy Exascale Earth System Model (E3SM) to enable online remapping strategies in order to simplify the coupled workflow process. We demonstrate the superior computational efficiency of the remapping algorithms in comparison with other state-of-the-science tools and present strong scaling results on large-scale machines for computing remapping weights between the spectral element atmosphere and finite volume discretizations on the polygonal ocean grids.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗
DOE OSTI2015

Maternal blood metal levels and fetal markers of metabolic function

Exposure to metals commonly found in the environment has been hypothesized to be associated with measures of fetal growth but the epidemiological literature is limited. The Maternal–Infant Research on Environmental Chemicals (MIREC) study recruited 2001 women during the first trimester of pregnancy from 10 Canadian sites. Our objective was to assess the association between prenatal exposure to metals (lead, arsenic, cadmium, and mercury) and fetal metabolic function. Average maternal metal concentrations in 1st and 3rd trimester blood samples were used to represent prenatal metals exposure. Leptin and adiponectin were measured in 1363 cord blood samples and served as markers of fetal metabolic function. Polytomous logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between metals and both high (≥90%) and low (≤10%) fetal adiponectin and leptin levels. Leptin levels were significantly higher in female infants compared to males. A significant relationship between maternal blood cadmium and odds of high leptin was observed among males but not females in adjusted models. When adjusting for birth weight z-score, lead was associated with an increased odd of high leptin. No other significant associations were found at the top or bottom 10th percentile in either leptin or adiponectin models. This study supports the proposition that maternal levels of cadmium influence cord blood adipokine levels in a sex-dependent manner. Further investigation is required to confirm these findings and to determine how such findings at birth will translate into childhood anthropometric measures. - Highlights: • We determined relationships between maternal metal levels and cord blood adipokines. • Cord blood leptin levels were higher among female than male infants. • Maternal cadmium was associated with elevated leptin in male, not female infants. • No significant associations were observed between metals and cord blood adiponectin. • Further work will elucidate the implications of these findings on childhood obesity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

NDARC - NASA Design and Analysis of Rotorcraft Validation and Demonstration

Validation and demonstration results from the development of the conceptual design tool NDARC (NASA Design and Analysis of Rotorcraft) are presented. The principal tasks of NDARC are to design a rotorcraft to satisfy specified design conditions and missions, and then analyze the performance of the aircraft for a set of off-design missions and point operating conditions. The aircraft chosen as NDARC development test cases are the UH-60A single main-rotor and tail-rotor helicopter, the CH-47D tandem helicopter, the XH-59A coaxial lift-offset helicopter, and the XV-15 tiltrotor. These aircraft were selected because flight performance data, a weight statement, detailed geometry information, and a correlated comprehensive analysis model are available for each. Validation consists of developing the NDARC models for these aircraft by using geometry and weight information, airframe wind tunnel test data, engine decks, rotor performance tests, and comprehensive analysis results; and then comparing the NDARC results for aircraft and component performance with flight test data. Based on the calibrated models, the capability of the code to size rotorcraft is explored.

Johnson, Wayne↗
DOE OSTI2014

TH-A-19A-02: Expanding TOPAS Towards Biological Modeling

Purpose: To expand Monte Carlo simulations of TOPAS to directly calculate radiobiological effects using various biological models. Methods: We use the TOPAS toolkit version beta9 and expand the provided scoring system to directly include scorers for relative biological effect (RBE) modeling. A scorer is a class to record a given physical property in the Monte Carlo simulations. We developed a scorer for the dose averaged linear energy transfer (LETd). This scorer was used to compare RBE weighted dose distributions assuming a constant RBE of 1.1 with the RBE weighted dose using a variable RBE based on a model using LETd and dose as input. Additionally, the local effect model (LEM) was implemented in TOPAS to estimate RBE weighted dose distributions. Survival curves for the LEM were simulated for a monoenergetic proton beams impinging on phantoms and for full patient treatment plans. Results: We have implemented radiobiological effect models in TOPAS, providing users with the possibility to directly obtain parameters related to radiobiological dose and use those as additional information to assess the quality of treatment plans. We applied the scorers to clinical patient treatments and phantom simulations. We find that for the proton therapy plans investigated the regions with the highest LETd are generally outside of the target volume, coinciding with the distal edge of treatment fields. LETd-based variable RBE weighted dose models predict higher doses at the distal edge of over 10% of the prescribed dose than when assuming a constant RBE. Conclusion: By implementing radiobiological effect models into a Monte Carlo simulation package we combine accurate dose calculation with biological modeling. TOPAS thereby provides distributions of multiple physical quantities such as the dose and LETd, and estimates of biological parameters (RBE, survival) thus allowing direct comparison of biological effective dose predictions between different models.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1974

The contamination of lunar highland rocks by KREEP - Interpretation by mixing models

A mixing model method was used to determine the component abundance in the Apollo 16 and 17 soils. This method uses the chemical composition of the soils for up to 30 elements and a weighted least-squares mixing model technique. Elements included in the calculations are: Si, Ti, Al, Ca, Fe, Mg, P, Cr, Mn, Na, K, Rb, Ba, U, Th, La, Ce, Sm, Eu, Sr, Yb, Y, Sc, V, Zr, Nb, Co, Ni, Li, Au, and Ir. The method was used to examine the possibility that some of the highland rocks such as VHA and low-K Fra Mauro basalt are mixtures. The results of the mixing model calculations show that it is possible that these rocks are mixtures of KREEP, troctolite, 'anorthosites', and a meteoritic component. The Rb-Sr systematics are consistent with such a model for the genesis of the highland rocks. KREEP has high relative concentrations of Rb, U, and radiogenic Sr and Pb and its model age of about 4.4 AE dominates the model age of all the 'contaminated' highland rocks.

Schonfeld, E.↗
NASA NTRS2013

Follow on Research for Multi-Utility Technology Test Bed Aircraft at NASA Dryden Flight Research Center (FY13 Progress Report)

Modern aircraft employ a significant fraction of their weight in composite materials to reduce weight and improve performance. Aircraft aeroservoelastic models are typically characterized by significant levels of model parameter uncertainty due to the composite manufacturing process. Small modeling errors in the finite element model will eventually induce errors in the structural flexibility and mass, thus propagating into unpredictable errors in the unsteady aerodynamics and the control law design. One of the primary objectives of Multi Utility Technology Test-bed (MUTT) aircraft is the flight demonstration of active flutter suppression, and therefore in this study, the identification of the primary and secondary modes for the structural model tuning based on the flutter analysis of MUTT aircraft. The ground vibration test-validated structural dynamic finite element model of the MUTT aircraft is created in this study. The structural dynamic finite element model of MUTT aircraft is improved using the in-house Multi-disciplinary Design, Analysis, and Optimization tool. In this study, two different weight configurations of MUTT aircraft have been improved simultaneously in a single model tuning procedure.

multi-utility technology test bed aircraft↗
DOE OSTI2021

Fixed Bit Rotary Drilling Failure Criteria Effect on Drilling Vibration

Polycrystalline diamond compact (PDC) bit use has been expanded to geothermal drilling applications due to their improved performance in drilling compared to roller cone bits. Multiple vibration models have been developed yet and there are limited use of field data to study the ability of numerical vibration model to predict recorded vibration in the field. The objective of this paper is to evaluate the capabilities of two different rock-bit interaction models with two different ROP formulations for predicting the actual behavior seen in the field. The first model (Model-I) considers the rock-bit interaction, incorporating the effect of subsurface formation characteristics. The second model (Model-II) is a PDC bit model that uses calculated threshold weight-on-cutter (WOCt) to study the interaction of the bit with the rock surface. The models, which couple the drill stem axial and torsional motions, are solved numerically using the fourth-order Runge-Kutta method and compared with recorded downhole drilling data from the Chocolate Mountain Aerial Gunnery Range (CMAGR) geothermal well located in California. An 8–½" PDC bit with 8 blades was modeled and vibration simulation was performed to investigate stick-slip vibrations at 1400 ft. depth. Simulation results using field data indicate that Model-II encounters a low range of stick-slip severity (SSV) as compared to Model-I, which is in the moderate range. Comparison with the field value for SSV shows Model-II is the best representative of the field case for this depth. The sensitivity analysis shows that results are sensitive to model constants making prediction of SSV from models uncertain.

15 GEOTHERMAL ENERGY↗
NASA NTRS1993

Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. II - Emission-source and generalized weighting-function properties of a time-dependent cloud-radiation model

The foremost physical aspects of microphysical-radiative interactions that control the formation and modulation of frequency-dependent passive microwave TB over a continental cold-rain precipitation system are explained within the framework of the essential ingredients of a physically based precipitation-retrieval algorithm. The analysis is based on a modeling study in which a 3D cloud model has provided the objective basis for generating an extensive set of microphysical profiles that describe numerous precipitation features in the course of the evolution of a continental hail storm. A summary of the various components of the algorithm, as well as the surface rain rates, is given. The algorithm employs the cloud model to provide a consistent and objectively generated source of detailed microphysical information as the underpinnings to an inversion-based perturbative retrieval scheme.

Mugnai, Alberto↗
NASA NTRS2017

Wind Tunnel Strain-Gage Balance Calibration Data Analysis Using a Weighted Least Squares Approach

A new approach is presented that uses a weighted least squares fit to analyze wind tunnel strain-gage balance calibration data. The weighted least squares fit is specifically designed to increase the influence of single-component loadings during the regression analysis. The weighted least squares fit also reduces the impact of calibration load schedule asymmetries on the predicted primary sensitivities of the balance gages. A weighting factor between zero and one is assigned to each calibration data point that depends on a simple count of its intentionally loaded load components or gages. The greater the number of a data point's intentionally loaded load components or gages is, the smaller its weighting factor becomes. The proposed approach is applicable to both the Iterative and Non-Iterative Methods that are used for the analysis of strain-gage balance calibration data in the aerospace testing community. The Iterative Method uses a reasonable estimate of the tare corrected load set as input for the determination of the weighting factors. The Non-Iterative Method, on the other hand, uses gage output differences relative to the natural zeros as input for the determination of the weighting factors. Machine calibration data of a six-component force balance is used to illustrate benefits of the proposed weighted least squares fit. In addition, a detailed derivation of the PRESS residuals associated with a weighted least squares fit is given in the appendices of the paper as this information could not be found in the literature. These PRESS residuals may be needed to evaluate the predictive capabilities of the final regression models that result from a weighted least squares fit of the balance calibration data.

calibration analysis↗
DOE OSTI2014

Multiscale modeling of polyisoprene on graphite

The local dynamics and the conformational properties of polyisoprene next to a smooth graphite surface constructed by graphene layers are studied by a multiscale methodology. First, fully atomistic molecular dynamics simulations of oligomers next to the surface are performed. Subsequently, Monte Carlo simulations of a systematically derived coarse-grained model generate numerous uncorrelated structures for polymer systems. A new reverse backmapping strategy is presented that reintroduces atomistic detail. Finally, multiple extensive fully atomistic simulations with large systems of long macromolecules are employed to examine local dynamics in proximity to graphite. Polyisoprene repeat units arrange close to a parallel configuration with chains exhibiting a distribution of contact lengths. Efficient Monte Carlo algorithms with the coarse-grain model are capable of sampling these distributions for any molecular weight in quantitative agreement with predictions from atomistic models. Furthermore, molecular dynamics simulations with well-equilibrated systems at all length-scales support an increased dynamic heterogeneity that is emerging from both intermolecular interactions with the flat surface and intramolecular cooperativity. This study provides a detailed comprehensive picture of polyisoprene on a flat surface and consists of an effort to characterize such systems in atomistic detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

Design Guide for Aerodynamics Testing of Earth and Planetary Entry Vehicles in a Ballistic Range

The purpose of this manual is to aid in the design of an aerodynamics test of an earth or planetary entry capsule in a ballistic range. In this manual, much use is made of the results and experience gained in 50 years of ballistic range aerodynamics testing at the NASA Ames Research Center, and in particular, that gained in the last 27 years, while the author was working at NASA Ames. The topics treated herein include: Data to be obtained; flight data needed to design test; Reynolds number and dynamic similarity of flight trajectory and ballistic range test; capabilities of various ballistic ranges; Calculations of swerves due to average and oscillating lift and of drag-induced velocity decreases; Model and sabot design; materials, weights and stresses; Sabot separation; Launches at angle of attack and slapping with paper to produce pitch/yaw oscillations.

Test Design↗
NASA NTRS1992

Mechanisms of nascent fiber formation during avian skeletal muscle hypertrophy

This study examined two putative mechanisms of new fiber formation in postnatal skeletal muscle, namely longitudinal fragmentation of existing fibers and de novo formation. The relative contributions of these two mechanisms to fiber formation in hypertrophying anterior latissimus dorsi (ALD) muscle were assessed by quantitative analysis of their nuclear populations. Muscle hypertrophy was induced by wing-weighting for 1 week. All nuclei formed during the weighting period were labeled by continuous infusion of 5-bromo-2'-deoxyuridine (BrdU), a thymidine analog, and embryonic-like fibers were identified using an antibody to ventricular-like embryonic (V-EMB) myosin. The number of BrdU-labeled and unlabeled nuclei in V-EMB-positive fibers were counted. Wing-weighting resulted in significant muscle enlargement and the appearance of many V-EMB+ fibers. The majority of V-EMB+ fibers were completely independent of mature fibers and had a nuclear density characteristics of developing fibers. Furthermore, nearly 100% of the nuclei in independent V-EMB+ fibers were labeled. These findings strongly suggest that most V-EMB+ fibers were nascent fibers formed de novo during the weighting period by satellite cell activation and fusion. Nascent fibers were found primarily in the space between fascicles where they formed a complex anastomosing network of fibers running at angles to one another. Although wing-weighting induced an increase in the number of branched fibers, there was no evidence that V-EMB+ fibers were formed by longitudinal fragmentation. The location of newly formed fibers in wing-weighted and regenerating ALD muscle was compared to determine whether satellite cells in the ALD muscle were unusual in that, if stimulated to divide, they would form fibers in the inter- and intrafascicular space. In contrast to wing-weighted muscle, nascent fibers were always found closely associated with necrotic fibers. These results suggest that wing-weighting is not simply another model of regeneration, but rather produces a unique environment which induces satellite cell migration and subsequent fiber formation in the interfascicular space. De novo fiber formation is apparently the principal mechanism for the hyperplasia reported to occur in the ALD muscle undergoing hypertrophy induced by wing-weighting.

Non-NASA Center↗
NASA NTRS1998

Comparison of Graphite Fabric Reinforced PMR-15 and Avimid N Composites After Long Term Isothermal Aging at Various Temperatures

Extensive effort is currently being expended to demonstrate the feasibility of using high-performance, polymer-matrix composites as engine structural materials over the expected operating lifetime of the aircraft, which can extend from 18,000 to 30,000 hr. The goal is to develop light-weight, high-strength, and high-modulus materials for use in higher temperature sections of advanced 21 st century aircraft propulsion systems. To accomplish this goal, it is necessary to pursue the development of thermal and mechanical durability models for graphite-fiber-reinforced, polymer-matrix composites. Numerous investigations have been reported regarding the thermo-oxidative stability (TOS) of the polyimide PMR-15 (1-5). A significant amount of this work has been directed at edge and geometry effects, reinforcement fiber influences, and empirical modeling of high-temperature weight loss behavior. It is yet to be determined if the information obtained from the PMR-15 composite tests is applicable to other polyimide-matrix composites. The condensation-curing polymer Avimid N is another advanced composite material often considered for structural applications at high temperatures. Avimid N has better thermo-oxidative stability than PMR-15 (6), but the latter is more easily processed. The most comprehensive study of the thermo-oxidative stability of Avimid N neat resin and composites at 371 (infinity)C is found in Salin and Seferis (7). The purposes of the work described herein were to compare the thermal aging behavior of these two matrix polymers and to determine the reasons for and the consequences of the difference in thermal durability. These results might be of some use in improving future polymer development through the incorporation of the desirable characteristics of both polyimides.

Bowles, Kenneth J.↗
NASA NTRS1990

Systems engineering and integration: Cost estimation and benefits analysis

Space Transportation Avionics hardware and software cost has traditionally been estimated in Phase A and B using cost techniques which predict cost as a function of various cost predictive variables such as weight, lines of code, functions to be performed, quantities of test hardware, quantities of flight hardware, design and development heritage, complexity, etc. The output of such analyses has been life cycle costs, economic benefits and related data. The major objectives of Cost Estimation and Benefits analysis are twofold: (1) to play a role in the evaluation of potential new space transportation avionics technologies, and (2) to benefit from emerging technological innovations. Both aspects of cost estimation and technology are discussed here. The role of cost analysis in the evaluation of potential technologies should be one of offering additional quantitative and qualitative information to aid decision-making. The cost analyses process needs to be fully integrated into the design process in such a way that cost trades, optimizations and sensitivities are understood. Current hardware cost models tend to primarily use weights, functional specifications, quantities, design heritage and complexity as metrics to predict cost. Software models mostly use functionality, volume of code, heritage and complexity as cost descriptive variables. Basic research needs to be initiated to develop metrics more responsive to the trades which are required for future launch vehicle avionics systems. These would include cost estimating capabilities that are sensitive to technological innovations such as improved materials and fabrication processes, computer aided design and manufacturing, self checkout and many others. In addition to basic cost estimating improvements, the process must be sensitive to the fact that no cost estimate can be quoted without also quoting a confidence associated with the estimate. In order to achieve this, better cost risk evaluation techniques are needed as well as improved usage of risk data by decision-makers. More and better ways to display and communicate cost and cost risk to management are required.

Dean, ED↗
NASA NTRS2024

Evaluating Differences Among Crop Models in Simulating Soybean in-Season Growth

Crop models are useful tools for simulating agricultural systems that require continued model development and testing to increase their robustness and improve how they describe our current understanding of processes. Coordinated and “blind” evaluation of multiple models using same protocols and experimental datasets provides unique opportunities to further improve models and enhance their reliability. For soybean [Glycine max (L.) Merr.], there has been limited coordinated multi-model evaluations for the simulation of in-season plant growth dynamics. We evaluated ten dynamic soybean crop models for their simulation of in-season plant growth using data from five experiments conducted in Argentina, Brazil, France, and USA. We evaluated models after a Blind (using only phenology data) and a Full calibration (with in-season and end-of-season variables). Calibration reduced model uncertainty by reducing standard bias for the simulation of in-season variables (biomass, leaf, pod, and stem weights, and leaf area index, LAI). However, we found that most models had difficulty in reproducing leaf growth dynamics, with normalized root mean squared error (nRMSE) of 56% for leaf weight and 43% for LAI (across locations and models after Full calibration). Models with different levels of complexity and experience were capable of simulating final seed yield at maturity with reasonable accuracy (nRMSE of 8–31% after Full calibration). However, the nRMSE for pod weight (of 17–64% after Full calibration) was two-fold larger than that of seed yield. Moreover, the models differed in how they simulated timing from sowing to beginning seed growth (47–93 days) and effective seed filling period (18–54 days), owing to model structural differences in defining the reproductive developmental stages. Overall, we identified the following processes that can benefit from further model improvement: leaf expansion and senescence, reproductive phenology, and partitioning to reproductive growth. Simulation of pod wall tissue and individual seed cohorts is another aspect that many models currently lack. Model improvement can benefit from high-temporal resolution experimental datasets that concurrently account for phenology, plant growth, and partitioning. Further, we recommend collecting reproductive phenology in the field consistent with actual dry matter allocation to organs in the models and collecting multiple observations of seed and pod weight to aid model improvement for simulation of seed growth and yield formation.

Agricultural Model Intercomparison and Improvement↗
DOE OSTI2025

Achieving GPT-4o level performance in astronomy with a specialized 8B-parameter large language model

AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, cosmology, and astronomical instrumentation. Trained on the complete collection of astronomy-related arXiv papers from 2007 to 2024 along with millions of synthetically-generated question-answer pairs and other astronomical literature, AstroSage-Llama-3.1-8B demonstrates remarkable proficiency on a wide range of questions. AstroSage-Llama-3.1-8B scores 80.9% on the AstroMLab-1 benchmark, greatly outperforming all models—proprietary and open-weight—in the 8-billion parameter class, and performing on par with GPT-4o. This achievement demonstrates the potential of domain specialization in AI, suggesting that focused training can yield capabilities exceeding those of much larger, general-purpose models. AstroSage-Llama-3.1-8B is freely available, enabling widespread access to advanced AI capabilities for astronomical education and research.

AI assistant↗
DOE OSTI2020

Evolutionary reinforcement learning of dynamical large deviations

In this work, we show how to bound and calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning. An agent, a stochastic model, propagates a continuous-time Monte Carlo trajectory and receives a reward conditioned upon the values of certain path-extensive quantities. Evolution produces progressively fitter agents, potentially allowing the calculation of a piece of a large-deviation rate function for a particular model and path-extensive quantity. For models with small state spaces, the evolutionary process acts directly on rates, and for models with large state spaces, the process acts on the weights of a neural network that parameterizes the model's rates. This approach shows how path-extensive physics problems can be considered within a framework widely used in machine learning.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗